Reenable the distributed checkpointing test #8424
Merged
+12
−3
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This is follow up of #8386.
In the previous pr I found that someone during fallback the pytorch will try to update an existing XLATensor with a CPU tesnor with different shape. In that case we need to remove the sharding spec otherwise there will be a shape mismatch. However I found that in the distributed point we will swap the existing XLATensor with the cpu tensor and it seems like we want to keep the sharding spec.
@jonb377 one concern I have is that test only test the single host, I felt like if it is a actual multi-host case the CPU tensor withh have different shape(sharded) than the shardingspec? I am not sure if we have such test somewhere. Even if we clear the shardingspec after a
torch_xla.sync()
the tensor will be moved to the device, but most likely replicated. I am a bit worried if I am breaking the distributed checkpointing here.